Every organization considering AI often has the same internal conversation at some point. The technology looks compelling. The ROI case is there. But then someone asks: how long will this actually take to stand up?
It's a reasonable concern. Enterprise software implementations have earned their reputation. Multi-month timelines, extensive data migrations, training cycles, and the inevitable integration surprises. These are real experiences that make any team cautious about committing to another platform.
The assumption, often unstated, is that AI works the same way. That getting it right requires extensive groundwork: rebuilding data structures, retraining staff, rebuilding workflows from scratch. But it doesn't have to be.
The dilemma of disruption in Life Sciences
Pharmaceutical and life sciences organizations face a version of this challenge that's particularly acute. In MLR, teams are highly specialized. Review processes are tightly governed. Workflows are built around existing infrastructure (often Veeva Vault PromoMats) that has been configured over years to meet regulatory requirements. The bar for disruption is essentially zero.
At the same time, MLR review is under pressure. Content volumes are growing. Review cycles are long. And the complexity of promotional review — spanning claim substantiation, regulatory compliance, fair balance, and brand adherence — doesn't leave much room for a parallel track while a new tool is being stood up.
This creates a genuine dilemma. The teams that need AI most are often the ones least positioned to absorb a disruptive implementation. If a new tool requires months of configuration, an integration project, and a large change management effort before it delivers any value, it's not really solving the problem. It's just moving it.
Moreover, most new tools require meaningful setup before they deliver value, from data migration to system integration or workflow redesign. For MLR teams, that timeline has a real cost: reviews don't pause while a new tool gets configured, and the team absorbing the implementation is the same team that was already stretched.
What if there was a better way to implement new AI solutions within life sciences?
How Revisto approaches it
Revisto was designed with the goal to make it as easy as possible for teams to get started without interrupting anything that's already working.
Because of this, onboarding is structured so that if a team can conduct MLR reviews today, they have everything required to get started. No data reformatting, no transformation, no new data structures.
Customers share the materials they already have — product and labeling documentation, compliance standards and SOPs, claims and reference documents, brand guidelines — in whatever format they exist today. Revisto ingests those materials directly and configures the system to the organization's specific requirements. And, because Revisto Companion can be embedded natively within Veeva Vault PromoMats or used as a standalone platform that integrates with Veeva (Revisto Studio), customers can adopt Revisto into their workflow in the way that is least disruptive to them.
The result is AI implementation without interrupting anything that's already working. Teams move through four structured phases: setup and data gathering, configuration and integration, piloting, and go-live, providing ROI in 6 to 8 weeks from engagement kick-off. The lift on the customer side is deliberately minimal at every stage.
However, this is not a claim about the technology being simple. MLR review is complex, and the AI driving Revisto's analysis is purpose-built for that complexity. But complexity in the model doesn't have to mean complexity in the implementation.
Want to see what implementation looks like in practice? [Read the Vericel case study →]